To celebrate Pride month, I was interested in looking into the Census data for our region about the LGBTQ community.
From https://www.census.gov/content/dam/Census/library/publications/2021/acs/acsbr-005.pdf: "As the largest available nationally representative household survey, the American Community Survey (ACS) is useful for examining the characteristics of households and families. Near the beginning of the survey questionnaire, there is a question asking how each household member is related to the householder. The householder is typically someone who owns the home or whose name is on the lease. Understanding each member’s relationship to the householder allows us to distinguish various types of household composition and family structure.
Definitions: Same-sex couple households: These households are identified using the “same-sex spouse” or “same-sex unmarried partner” selections to the relationship to householder question. Total coupled households: The total of those households in which the householder reports having a spouse or unmarried partner in the household. Includes opposite-sex and samesex married and unmarried couples. Unmarried partner: A person in an intimate relationship with the householder, such as a boyfriend or a girlfriend."
I wanted to look into the ACS 5-year (for good sample size) estimates for same-sex married and unmarried couples in the region and by county.
library(tidyverse)
library(sf)
library(leaflet)
library(tidycensus)
library(writexl)
library(htmlwidgets)
source('C:/Users/SChildress/Documents/GitHub/psrc-census-tools/library/psrc_census_config.R')
source('C:/Users/SChildress/Documents/GitHub/psrc-census-tools/library/psrc_census.R')
# See: https://www.census.gov/content/dam/Census/library/publications/2021/acs/acsbr-005.pdf
Sys.getenv("CENSUS_API_KEY")region_same_sex_married<-psrc_acs_table("B09019_011", "region", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 1 x 6
## variable total_region moe_region ACS_Year ACS_Type label
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 B09019_0~ 12259 625. 2019 acs5 Estimate!!Total:!!In hous~region_same_sex_cohabit<-psrc_acs_table("B09019_013", "region", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 1 x 6
## variable total_region moe_region ACS_Year ACS_Type label
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 B09019_0~ 7311 587. 2019 acs5 Estimate!!Total:!!In hous~# Regional Households
region_hhs<- psrc_acs_table("B08201_001", "region", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 1 x 6
## variable total_region moe_region ACS_Year ACS_Type label
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 B08201_001 1603060 3393. 2019 acs5 Estimate!!Total:# I should actually retrieve these values from the table above (lazy)
same_sex_share <-(12259+7311)/1603060
same_sex_share## [1] 0.0122079county_same_sex_married<-psrc_acs_table("B09019_011", "county", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 4 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King ~ B09019_0~ 7886 486 2019 acs5 county Estimat~
## 2 53035 Kitsa~ B09019_0~ 511 142 2019 acs5 county Estimat~
## 3 53053 Pierc~ B09019_0~ 2053 286 2019 acs5 county Estimat~
## 4 53061 Snoho~ B09019_0~ 1809 230 2019 acs5 county Estimat~
## # ... with 1 more variable: concept <chr>county_same_sex_cohabit<-psrc_acs_table("B09019_013", "county", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 4 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King ~ B09019_~ 5056 509 2019 acs5 county Estimate~
## 2 53035 Kitsa~ B09019_~ 282 93 2019 acs5 county Estimate~
## 3 53053 Pierc~ B09019_~ 1249 234 2019 acs5 county Estimate~
## 4 53061 Snoho~ B09019_~ 724 148 2019 acs5 county Estimate~
## # ... with 1 more variable: concept <chr>The data says that there are 12,300 same-sex married couples and 7,300 same-sex unmarried couples. This just seems way too low at around 1.2% of households. There are obviously many same-sex people who are living without partners, but regardless, it makes me question the data.
I retrieved the same sex households by Census Tract to see what the geographic results look like. Many tracts are within the MOE.
tract_same_sex_married<-psrc_acs_table("B09019_011", "tract", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 776 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033~ Censu~ B09019_~ 30 34 2019 acs5 tract Estima~
## 2 53033~ Censu~ B09019_~ 36 40 2019 acs5 tract Estima~
## 3 53033~ Censu~ B09019_~ 12 19 2019 acs5 tract Estima~
## 4 53033~ Censu~ B09019_~ 19 31 2019 acs5 tract Estima~
## 5 53033~ Censu~ B09019_~ 29 34 2019 acs5 tract Estima~
## 6 53033~ Censu~ B09019_~ 13 19 2019 acs5 tract Estima~
## 7 53033~ Censu~ B09019_~ 0 17 2019 acs5 tract Estima~
## 8 53033~ Censu~ B09019_~ 46 44 2019 acs5 tract Estima~
## 9 53033~ Censu~ B09019_~ 13 20 2019 acs5 tract Estima~
## 10 53033~ Censu~ B09019_~ 0 12 2019 acs5 tract Estima~
## # ... with 766 more rows, and 1 more variable: concept <chr>tract_same_sex_cohabit<-psrc_acs_table("B09019_013", "tract", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 776 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033~ Censu~ B09019_~ 11 19 2019 acs5 tract Estima~
## 2 53033~ Censu~ B09019_~ 19 32 2019 acs5 tract Estima~
## 3 53033~ Censu~ B09019_~ 22 24 2019 acs5 tract Estima~
## 4 53033~ Censu~ B09019_~ 0 17 2019 acs5 tract Estima~
## 5 53033~ Censu~ B09019_~ 0 17 2019 acs5 tract Estima~
## 6 53033~ Censu~ B09019_~ 0 12 2019 acs5 tract Estima~
## 7 53033~ Censu~ B09019_~ 25 29 2019 acs5 tract Estima~
## 8 53033~ Censu~ B09019_~ 40 40 2019 acs5 tract Estima~
## 9 53033~ Censu~ B09019_~ 34 29 2019 acs5 tract Estima~
## 10 53033~ Censu~ B09019_~ 13 14 2019 acs5 tract Estima~
## # ... with 766 more rows, and 1 more variable: concept <chr>ss_married_map<-create_tract_map(tract_same_sex_married)## Reading layer `dbo.tract2010_nowater' from data source `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes' using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## projected CRS: NAD83 / Washington North (ftUS)ss_cohabit_map<-create_tract_map(tract_same_sex_cohabit)## Reading layer `dbo.tract2010_nowater' from data source `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes' using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## projected CRS: NAD83 / Washington North (ftUS)ss_married_mapss_cohabit_map